In this study, a novel monolithic capillary column based on a NH2-MIL-53(Al) metal–organic framework (MOF) incorporated in poly (3-acrylamidophenylboronic acid/methacrylic acid-co-ethylene glycol dimethacrylate) (poly (AAPBA/MAA-co-EGDMA)) was prepared using an in situ polymerization method. The characteristics of the MOF-polymer monolithic column were investigated by scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, X-ray diffractometry, Brunauer-Emmett-Teller analysis, and thermogravimetric analysis. The prepared MOF-polymer monolithic column showed good permeability, high extraction efficiency, chemical stability, and good reproducibility. The MOF-polymer monolithic column was used for in-tube solid-phase microextraction (SPME) to efficiently adsorb trace sulfonamides from food samples. A novel method combining MOF-polymer-monolithic-column-based SPME with ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was successfully developed. The linear range was from 0.015 to 25.0 µg/L, with low limits of detection of 1.3–4.7 ng/L and relative standard deviations (RSDs) of < 6.1%. Eight trace sulfonamides in fish and chicken samples were determined, with recoveries of the eight analytes ranging from 85.7% to 113% and acceptable RSDs of < 7.3%. These results demonstrate that the novel MOF-polymer-monolithic-column-based SPME coupled with UHPLC-MS/MS is a highly sensitive, practical, and convenient method for monitoring trace sulfonamides in food samples previously extracted with an adequate solvent.

No abstract

No abstract

Machine learning systems have been extensively used as auxiliary tools in domains that require critical decision-making, such as healthcare and criminal justice. The explainability of decisions is crucial for users to develop trust on these systems. In recent years, the globally-consistent rule-based summary-explanation and its max-support (MS) problem have been proposed, which can provide explanations for particular decisions along with useful statistics of the dataset. However, globally-consistent summary-explanations with limited complexity typically have small supports, if there are any. In this paper, we propose a relaxed version of summaryexplanation, i.e., the 𝑞-consistent summary-explanation, which aims to achieve greater support at the cost of slightly lower consistency.The challenge is that the max-support problem of 𝑞-consistent summary-explanation (MSqC) is much more complex than the original MS problem, resulting in over-extended solution time using standard branch-and-bound solvers. To improve the solution time efficiency, this paper proposes the weighted column sampling (WCS) method based on solving smaller problems by sampling variables according to their simplified increase support (SIS) values. Experiments verify that solving MSqC with the proposed SIS-based WCS method is not only more scalable in efficiency, but also yields solutions with greater support and better global extrapolation effectiveness.

No abstract